Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially loc...Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis （PCA） and locally linear embedding （LIE） proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.展开更多
针对基于图的无监督特征选择算法存在挖掘数据内在信息不充分,且易受噪声干扰难以获取更具有判别性特征的问题,提出一种基于广义不相关回归和潜在表示学习的无监督特征选择方法(uncorrelated regression and latent representation for ...针对基于图的无监督特征选择算法存在挖掘数据内在信息不充分,且易受噪声干扰难以获取更具有判别性特征的问题,提出一种基于广义不相关回归和潜在表示学习的无监督特征选择方法(uncorrelated regression and latent representation for unsupervised feature selection,URLUFS)。该方法将非负矩阵分解作用于广义不相关回归模型的投影矩阵,使投影矩阵实现非线性的维数约简并获得特征选择矩阵。在特征选择矩阵的基础上,引入自适应图学习来进一步挖掘数据的局部流形结构,并对特征选择矩阵施加范数约束以保持稀疏性。利用潜在表示对数据样本间的相互关系进行学习,引导回归模型中的伪标签矩阵,从而选择出更具有判别性的特征。在8个公开的数据集上进行了数值对比实验,实验结果表明:基于广义不相关回归和潜在表示学习的无监督特征选择算法明显优于其他8种无监督特征选择算法。展开更多
文摘Many problems in image representation and classification involve some form of dimensionality reduction. Nonnegative matrix factorization （NMF） is a recently proposed unsupervised procedure for learning spatially localized, partsbased subspace representation of objects. An improvement of the classical NMF by combining with Log-Gabor wavelets to enhance its part-based learning ability is presented. The new method with principal component analysis （PCA） and locally linear embedding （LIE） proposed recently in Science are compared. Finally, the new method to several real world datasets and achieve good performance in representation and classification is applied.
基金国家自然科学基金(62272077,72301050)重庆市教委科技重大项目(KJZD-M202400604)资助+1 种基金the Natural ScienceFoundation of Chongqing,China(No.cstc2021jcyj msxmX0557)the MOE Layout Foundation of Humanities and Social Sciences,China(No.20YJAZH102).
文摘针对基于图的无监督特征选择算法存在挖掘数据内在信息不充分,且易受噪声干扰难以获取更具有判别性特征的问题,提出一种基于广义不相关回归和潜在表示学习的无监督特征选择方法(uncorrelated regression and latent representation for unsupervised feature selection,URLUFS)。该方法将非负矩阵分解作用于广义不相关回归模型的投影矩阵,使投影矩阵实现非线性的维数约简并获得特征选择矩阵。在特征选择矩阵的基础上,引入自适应图学习来进一步挖掘数据的局部流形结构,并对特征选择矩阵施加范数约束以保持稀疏性。利用潜在表示对数据样本间的相互关系进行学习,引导回归模型中的伪标签矩阵,从而选择出更具有判别性的特征。在8个公开的数据集上进行了数值对比实验,实验结果表明:基于广义不相关回归和潜在表示学习的无监督特征选择算法明显优于其他8种无监督特征选择算法。
